mlsanityData to 7 Oct 2026

Interactive4D: Interactive 4D LiDAR Segmentation

arXiv 2410.08206 ↗First posted 10 Oct 2024cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

Interactive segmentation has an important role in facilitating the annotation process of future LiDAR datasets. Existing approaches sequentially segment individual objects at each LiDAR scan, repeating the process throughout the entire sequence, which is redundant and ineffective. In this work, we propose interactive 4D segmentation, a new paradigm that allows segmenting multiple objects on multiple LiDAR scans simultaneously, and Interactive4D, the first interactive 4D segmentation model that segments multiple objects on superimposed consecutive LiDAR scans in a single iteration by utilizing the sequential nature of LiDAR data. While performing interactive segmentation, our model leverages the entire space-time volume, leading to more efficient segmentation. Operating on the 4D volume, it directly provides consistent instance IDs over time and also simplifies tracking annotations. Moreover, we show that click simulations are crucial for successful model training on LiDAR point clouds. To this end, we design a click simulation strategy that is better suited for the characteristics of LiDAR data. To demonstrate its accuracy and effectiveness, we evaluate Interactive4D on multiple LiDAR datasets, where Interactive4D achieves a new state-of-the-art by a large margin. We publicly release the code and models at https://vision.rwth-aachen.de/Interactive4D.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 42 of its 56 references that are in the map. First posted on arXiv 10 Oct 2024.

Builds on

42 of 56 references.

References · 42

  1. 2017Attention Is All You Needcited by 59,932
  2. 2023Segment Anythingcited by 8,158
  3. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  4. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  5. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  6. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  7. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
  8. 2019LVIS: A Dataset for Large Vocabulary Instance Segmentationcited by 1,215
  9. 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,201
  10. 2021KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3Dcited by 570
  11. 2018The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scalecited by 542
  12. 2020PointContrast: Unsupervised Pre-training for 3D Point Cloud Understandingcited by 502
30 more
  1. 2020PREDATOR: Registration of 3D Point Clouds with Low Overlapcited by 287
  2. 2022Language-Grounded Indoor 3D Semantic Segmentation in the Wildcited by 243
  3. 20183D-SIS: 3D Semantic Instance Segmentation of RGB-D Scanscited by 216
  4. 2021Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Trackingcited by 165
  5. 2021Reviving Iterative Training with Mask Guidance for Interactive Segmentationcited by 162
  6. 2016Deep Interactive Object Selectioncited by 160
  7. 2021Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentationcited by 107
  8. 2022FocalClick: Towards Practical Interactive Image Segmentationcited by 101
  9. 2020LiDAR-based Panoptic Segmentation via Dynamic Shifting Networkcited by 84
  10. 2021EfficientLPS: Efficient LiDAR Panoptic Segmentationcited by 76
  11. 20214D Panoptic LiDAR Segmentationcited by 64
  12. 2022Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmapcited by 59
  13. 2020MOPT: Multi-Object Panoptic Trackingcited by 53
  14. 2018Iteratively Trained Interactive Segmentationcited by 52
  15. 2022Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentationcited by 47
  16. 2020A Benchmark for LiDAR-based Panoptic Segmentation based on KITTIcited by 40
  17. 2021GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Networkcited by 37
  18. 2019Continuous Adaptation for Interactive Object Segmentation by Learning from Correctionscited by 36
  19. 20224D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregationcited by 35
  20. 2023AGILE3D: Attention Guided Interactive Multi-object 3D Segmentationcited by 33
  21. 2023Mask4Former: Mask Transformer for 4D Panoptic Segmentationcited by 24
  22. 2022Interactive Object Segmentation in 3D Point Cloudscited by 23
  23. 20234D Panoptic Segmentation as Invariant and Equivariant Field Predictioncited by 19
  24. 2023PUPS: Point Cloud Unified Panoptic Segmentationcited by 16
  25. 2023DynaMITe: Dynamic Query Bootstrapping for Multi-object Interactive Segmentation Transformercited by 14
  26. 20234D-Former: Multimodal 4D Panoptic Segmentationcited by 12
  27. 2022LiDAR-based 4D Panoptic Segmentation via Dynamic Shifting Networkcited by 11
  28. 2024JRDB-PanoTrack: An Open-world Panoptic Segmentation and Tracking Robotic Dataset in Crowded Human Environmentscited by 10
  29. 2023Lidar Panoptic Segmentation and Tracking without Bells and Whistlescited by 7
  30. 2024Scale Disparity of Instances in Interactive Point Cloud Segmentationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.